This module develops students' technical skills and theoretical knowledge to develop risk management analytics within an organisation using AI and Data Science. The module aims to augment their existing business knowledge with the technological skills needed to visualise, understand and interpret structured and non-structured data in an increasingly complex, automated and IT-driven environment. The availability of big and alternative data presents a challenge to businesses to utilise this information to enable the identification of trends, early warning signals, correlations, and preferences to facilitate informed decision-making and planning. Students will deploy machine learning applications to real-world risk management problems.

This module will teach the principles and applications of data science, ranging from data identification and collection through to model interpretations and decision-making. The students will develop an appreciation of the importance of the risk context and the need to develop a cohesive plan for analysis. Learners will make use of a variety of AI tools, models, and techniques to aid in capturing and managing risk, and better understand risk exposure. They will be introduced to key statistical concepts and probability theory, including an introduction to Bayesian probability and supervised learning and unsupervised learning methods. They will develop skills in data gathering, cleaning, exploration, and visualisation. The module will also develop their risk assessment skills and develop an appreciation of the role that risk assessment plays in making informed decisions in a finance, insurance, or business setting. Various risk modelling and simulation techniques will be introduced to better capture, understand, and analyse risk, while sensitivity and robustness techniques will be introduced to aid risk management decisions. Techniques for analysing and managing big data will be explored in addition to examining emerging trends in Data Science applications in risk management.

On successful completion of this module, students will be able to: • Clean and appraise the characteristics of gathered data. • Demonstrate the development of an ability to interpret and apply Bayesian statistics. • Apply data science and AI models and techniques to analyse risk exposure and risk management metrics • Critically assess the robustness and sensitivity of model outputs in a variety of dynamic settings and scenarios • Generate risk management decisions informed by appropriate data science tools, techniques and methodologies

On successful completion of this module, students will be able to: • Demonstrate an appreciation of the complexities and interdependencies of key variables in collected data. • Demonstrate an appreciation of the ethical concerns regarding the developments in AI and their potential impacts on society.

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The module will be taught through a combination of lectures, tutorials, guest speakers and lab sessions through which the student will acquire the technical skills and knowledge required to interrogate the data to yield useful insights. The module will support the development of courageous and responsible learners by engaging in discussions on modelling techniques and their applications in practice and society.